Field of the Invention
The present invention generally relates to wireless telecommunication networks, such as cellular networks.
Overview of the Related Art
The characterization and prediction of data and voice traffic trend is an issue of fundamental importance in the design and optimization of cellular networks.
Specifically, the inherent nature of the traffic associated to the nodes/cells in a cellular network has different typological features depending on the spatial position wherein such nodes/cells are located (e.g., residential area, commercial area, business area, and so on) and depending on the time evolution of the traffic itself (e.g., deployment of a new technology, relocation/increase of local resources, and so on). In particular, the type of traffic associated with a node of the cellular network that is located in a urbanized business area is different from the type of traffic associated with a network node that is located in a urbanized residential area, or in a touristic area. As the time resolution increases, traffic data evolution becomes more and more irregular, even if a typical, basic traffic trend component (hereinafter referred to as “intrinsic component of the traffic trend”, or briefly “intrinsic component”) can be noticed. As an example, the cellular traffic data trend typically has a minimum during the middle of the night, which rises from early hours of the morning until stabilizing in the middle of the day. An extremely variable traffic component, or “random component” is superimposed on this intrinsic component.
The ability to extrapolate the behavior of historical data sequences (intended as collections of data sequentially gathered in various modes, from raw data output from the sampler to data that is pre-processed through, for example, filtering operations) in future periods has gained momentum in various disciplines such as economics (e.g., forecasting of stock prices trends or of macroeconomic parameters of a nation), biology (e.g., the evolution of epidemics) or engineering (for example, aging of the materials). The extrapolation of the behavior in future times of historical data sequences acquired in the past, unless there is further information about future dynamics, is generally and implicitly based on the assumption that the analyzed phenomena is stationary. Different known predictive techniques that rely on this type of assumption are used, such as for example:                techniques based on “spline” extrapolation in which the derivative is maintained from the extrapolation point (see for example Hyndman, King, Pitrun, Billah, “Local linear forecast using cubic smoothing splines”, Aust. N.Z. Stat. 47(1), 2005, pages 87-99);        techniques based on neural networks in which each node learns from the behavior of the past (see for example Crone, Dhawan, “Forecasting seasonal time series with neural networks: a sensitivity analysis of architecture parameters”, Proceeding of the international joint conference on neural networks, Orlando, Fla., USA, Aug. 12-17, 2007, or Gheyas, Smith, “A neural network approach to time series forecasting”, Proceedings of the World Congress on Engineering 2009, Vol II, WCE 2009, Jul, 1-3, 2009, London, UK);        regressive techniques in which the more or less recent past is stored within weight parameters (see for example Barford, Kline, Plonka, Ron, “A signal analysis of network traffic anomalies”, IMW'02, November 2002, Gelper, Fried, Croux, “Robust forecasting with exponential and Holt-Winted smoothing”, Faculty of Economics and management, Katholieke Universitet Leuven, April 2007, or “Single, Double and Triple exponential Smoothing”, NIST, http ://www.itl.nist.gov/div898/handbook/pmc/section4).        
In general, the processing of data through the predictive techniques mentioned above requires a sampling of the real data, that is, a process that detects the signal to be treated in discrete time instants outputting as a result data which may be the instantaneous reading of the sampled quantity or a processed version thereof between two successive time instants (for example, the average value over the interval of sampling). Which data, inherently, may be affected by noise, may undergo a filtering process (for example a moving average) and could not be fully in line with the requirements of the Nyquist theorem (i.e., the data is sampled with a frequency that is established by the same sampling system without making a pre-analysis of the observed quantity in order to define the bandwidth and therefore the sampling frequency).
Some of these techniques are best suited to be applied to phenomena represented by historical sequences with predefined characteristics (e.g., historical sequences with seasonal trends). To apply these techniques it is therefore necessary to have a knowledge or a pre-classification of the considered phenomena in order to select the best method: for example, some techniques require to determine the seasonality period through the observation of historical data, before starting the forecasting process.
Within this framework, the random component, to be understood in a broad sense, as the set of statistical variations of the phenomenon that can overlap the intrinsic component of the phenomenon itself, is an important element to consider.
If the intrinsic component was hidden by the random component, some predictive techniques may decrease in efficiency; in these cases, techniques such as spectral analysis or self/cross correlation processes could help in distinguishing the intrinsic component from the random component so as to separate the former from the latter. Applicant has observed that the random component can represent a significant component of the phenomenon under consideration, also for forecasting purposes. For example, in mobile telecommunications, the random component represents local variations from the intrinsic component due to predictable or unpredictable spot service requests. An example of an unpredictable local traffic increase could be generated by a queue of cars waiting on a road because of a car accident, while an example of a predictable local traffic increase could be due to a sporting event.
US patent application US20100030545 discloses a pattern shape predicting method comprising: predicting, with simulation, an intensity distribution of a pattern image concerning a pattern shape of a pattern on substrate formed on a substrate based on pattern data; calculating a first pattern edge position from the intensity distribution of the pattern image; calculating a feature value of the intensity distribution of the pattern image in a predetermined range including the first pattern edge position; calculating a fluctuation amount of the first pattern edge position from the feature value using a correlation; and predicting a second pattern edge position taking into account the fluctuation amount with respect to the first pattern edge position.
International patent application WO00/30385 discloses a method and system in a wireless communications system which enables a network operator to accommodate subscriber demands by matching resources to current, congested traffic levels and future, predicted traffic levels. The accommodation may be effectuated at the cell level, and the applicable resources include transceivers. A given base station in a network records variables on preferably at least three occasions. The variables include time of measurement, mean traffic level, busy hour TL, and current number of TRXs. These four variables may then be used in a non-linear optimization formula applied to a growth equation. Three vital coefficients are produced for the equation. Future traffic levels, as well as a maximum expected subscriber population, for the given cell can be estimated from the growth equation with the vital coefficients. A possible busy hour TL and the predicted future TLs may be used to determine an optimized number of TRXs.
Paper Dorgbefu, Gadze, Anipa, “Short term traffic Volume prediction in UMTS networks using the Kalman Filter Algorithm”, International Journal of Mobile network Communications & Telematics, Vol. 3. No. 6. December 2013, describes a method for forecasting UMTS (Universal Mobile Telecommunications System) traffic through Kalman filtering techniques.